Executive Summary

In August 2010, the Agency for Healthcare Research and Quality (AHRQ) commissioned a study to conduct a proactive risk assessment of surgical site infections (SSIs) within the ambulatory surgery setting. The American Institutes for Research (AIR) and its partners, Anthony D. Slonim, M.D., Dr.P.H., and the Virginia Polytechnic Institute and State University's College of Engineering, were selected to conduct the study, which had two primary objectives: (1) using proactive risk assessment, identify the realm of risk factors associated with SSIs that result from procedures performed at ambulatory surgery centers (ASCs); and (2) based on a particular set of events identified by the proactive risk assessment, design an intervention to mitigate the probability of SSIs due to the most common risk factors.

To achieve a better understanding of how structural and process elements may affect the risk for SSIs in the ASC environment, we used a tool known as sociotechnical (or socio-technical) probabilistic risk assessment (ST-PRA) This approach incorporates risk estimates from the evidence-based literature and also uses experiential estimates from health care providers. ST-PRA is particularly helpful for estimating risks in outcomes that are very rare, such as the risk of SSI in the ambulatory surgery environment, and examining single-point failures as well as combinations of events that lead to the outcome of interest.

As a preliminary step, the team examined several data sources, both quantitative and qualitative in nature, including databases, peer-reviewed and grey literature, site visits to four local ASCs, and technical expert opinion.

Developing the ST-PRA Model: Steps

Step 1—Identify all factors (also known as basic events) contributing to an SSI. An initial list of basic events was created based on the major risk factors recognized in the extant literature as contributing to an SSI.

Step 2—Identify the dependencies and interactions among risk points. The research team separated the basic events into components of the operative process (i.e., preoperative, operative, and postoperative) and examined the relationships (i.e., dependencies and interactions) among the multiple risk points to understand how they collectively lead to an SSI.

Step 3—Validate the fault tree model. The underlying logic of the model was validated by obtaining feedback from technical experts on the model's representation of the real system and processes under study (e.g., the preoperative, operative, and postoperative processes for an arthroscopic knee surgery at the ASC).

Step 4—Identify the likelihood of the basic events in the fault tree. We assigned probabilities to each basic event in the fault tree, using information available from the literature and interviews. This process resulted in a probability of occurrence of the top-level event, along with the major risk points in the process (also known as cut sets) that were developed as the next step of this project.

Step 5—Conduct sensitivity analyses of the fault tree model. We conducted a series of sensitivity analyses to improve the reliability of the modeling exercise due to variable or imprecise information available from the databases, variable information in the literature, and expert estimates. These analyses involved identifying the minimal cut sets for the base case and for each variation of the base case (obtained by modifying the probabilities) in order to study the robustness of the fault tree model.

Designing a Risk-informed Intervention

Next, we examined the events from the ST-PRA model ranked in order of criticality, finding that Event 642 [Fail to protect the patient effectively] ranked as the most critical unique event, with the highest independent contribution to the occurrence of SSIs of 0.5187. Based on this finding, we propose an intervention aimed at Event 642 [Fail to protect patient effectively] that focuses on all five major components of this cut set. Specifically, the intervention is designed to target: skin preparation practices; proper administration of antibiotics; staff training in infection control practices; practices to prevent glove punctures; and procedures to ensure removal of watches, jewelry, and fake nails.

The proposed intervention targets two important processes of patient care:

Infection control practices. A major aspect of the intervention involves integrating better standards for infection prevention practices into the daily care provided at ASCs. We recommend that guidelines for infection control practices at ASCs be modeled after the guidelines provided to hospitals and incorporated into processes of care as a bundle of procedures and/or checklist of steps.

Communications between health care providers. The next piece of the intervention involves improving the communications across the various providers, including the physicians, surgeons, and ASC preoperative, operative, and postoperative staff. We propose that efforts also be directed to improve the communications between health care teams to more readily identify those patients who would receive more appropriate care in an alternative environment, such as the hospital, where they have the tools and techniques in place to better care for patients such as the morbidly obese.

Next Steps

The use of ST-PRA as a modeling tool to identify risks in the ASC environment is an important outcome of this work. This model can be refined as new information becomes available in the literature and as improvements in care in the ASC environment are realized through interventions such as those proposed in this report. AHRQ should consider the following suggestions for next steps to continue this work: developing the proposed intervention, conducting a followup study to determine the impact of the intervention, developing an integrated database to track patients across care settings, and examining ways to make the ST-PRA methodology more accessible.